Towards sustainable water management in an arid agricultural region: A multi-level multi-objective stochastic approach

Zhang, Fan; Guo, Shanshan; Liu, Xiao; Wang, Youzhi; Engel, Bernard A.; Guo, Ping

doi:10.1016/j.agsy.2020.102848

Cited by 40 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multi-objective programming (MOP) is a valuable tool for helping decision makers facilitate decision-making with multiple conflicting objectives (Fang et al, 2018b;Guo et al, 2020c), which can offer feasible methods for generating compromise decision alternatives. Some MOP approaches have been widely developed to tackle the uncertainty associated with the decision-making processes, such as multi-objective fuzzy programming (Zimmermann, 1978;Pishvaee and Razmi, 2012;Ren et al, 2017) and multiobjective stochastic programming (Xu et al, 2014(Xu et al, , 2020Zhang et al, 2020). These approaches generally convert the multi-objective functions into a single-objective deterministic problem through a fuzzy programming method or a constraint operator.…”

Section: Introductionmentioning

confidence: 99%

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Guo

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

show abstract

Section: Introductionmentioning

confidence: 99%

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Guo

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Environmental sciences, engineering, and agricultural sciences comprise the main subareas, grouping 66% of the works. The primary authors related to the problem of water allocation using the MSP technique are Huang G. H, Li Y. P, and Luocks D. P. In addition, Zhang et al state a categorization for water decision-makers, considering their role in the allocation and their impact on the entire supply chain [16]. 1.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the final decision-makers (farmers) focus on obtaining the most significant benefit possible. Therefore, farmers have the highest risk levels in the system [16]. There are few but significant effects of a proper water allocation plan on the farmer's benefits [9,[19][20][21], regarding that most of the works address the first and second-level decision-makers.…”

Section: First Guiding-question Answermentioning

confidence: 99%

Multistage Stochastic Programming to Support Water Allocation Decision-Making Process in Agriculture: A Literature Review

Marquez

Sarmiento

Lamos

2022

The 1st International Online Conference on Agriculture—Advances in Agricultural Science and Technology

View full text Add to dashboard Cite

show abstract

“…Zhang et al [33] used a multi-level multi-objective stochastic model to examine the sustainable management of water resources in an arid zone for agriculture in the northwest of China. They proposed a new optimization modeling approach consisting of a Multi-Level Multi-Objective Stochastic Programming (MLMOSP) and used the weight quantification method to formulate the sustainable water allocation of that area.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Meta-Heuristic for a Bi-Objective Stochastic Optimization of Urban Water Supply System

et al. 2020

View full text Add to dashboard Cite

The restoration and remodeling of the urban water supply system are traditional challenges for water companies due to either aged existing water supply networks or lodging expansion. These challenges involve the uncertainties induced by their lengthy-planned prospects and the impossible exact prediction of forthcoming events. In this regard, correlations exacerbate unpredictable data and parameters and probably undermine taking effective decisions in this context. Therefore, the remodel and restoration decision of water supply systems must be made using approaches that can effectively deal with correlation uncertainties. The present study develops a bi-objective stochastic optimization model that can handle interrelated uncertain parameters in the water supply system remodeling and restoration issue. The proposed mathematical model is validated using the data of the Mashhad Plain water supply system as a real case study, followed by performing and comparing different levels of conservatism and reliability. As a complex optimization problem, an efficient algorithm is needed to solve the problem. To this end, a hybrid meta-heuristic algorithm, which is a combination of the Red Deer Algorithm (as a newly introduced nature-inspired heuristic) and Simulated Annealing (as a traditional local search algorithm), is proposed. Considering the advantages of these algorithms, it is possible to alleviate the disadvantages of current methods when solving large-scale networks. Finally, an extensive comparison and discussion are made and then the main findings with practical solutions are presented to significantly evaluate the proposed model and algorithm.

show abstract

Towards sustainable water management in an arid agricultural region: A multi-level multi-objective stochastic approach

Cited by 40 publications

References 29 publications

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Multistage Stochastic Programming to Support Water Allocation Decision-Making Process in Agriculture: A Literature Review

A Hybrid Meta-Heuristic for a Bi-Objective Stochastic Optimization of Urban Water Supply System

Contact Info

Product

Resources

About